Bibliographic record
Abstract
This article describes transit systems’ transition from traditional customer service to customer relationship management (CRM), which emphasizes building and enhancing relationships with customers through meeting their needs while managing costs. To manage costs, agencies are using the Web and text messaging to reduce call volume and are using CRM software and workflow changes to be more responsive to commendations, complaints, and suggestions. New Jersey Transit took the opportunity of removing its toll-free number (because most customers were already making the calls at no charge; the TTY line remained) to steer customers toward its Web site and wireless messaging options that deliver transit schedules to cell phones. It also installed a commercial customer service workflow software system and reorganized its customer service operations. Web forms and other innovations cut response time by more than 35 percent and increased the volume of interactions more than fivefold. The San Francisco Municipal Transportation Agency partnered with the City and County of San Francisco’s 3-1-1 Customer Service Center to replace its separate contact center. That, too, has a CRM software system. Translink, Metro Vancouver’s transit agency, also uses a CRM system. In addition, it uses NextBus to provide automatic text response to Web inquiries. A key performance measure for CRM systems is “first contact resolution (FCR).” Portland, Oregon’s TriMet increased its FCR from a low of 59 percent to 77 percent using CRM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".